Humans possess a remarkable ability to parse images simply by looking at them.
In a blink of an eye, a human can fully analyze an image and separate all its components.
People can perform several tasks simultaneously by analyzing an image, e.g., object detection and contour detection.
Furthermore, humans can easily generalize from observing a set of objects to recognizing objects that have never been seen before.
Although humans enjoy an inherent capacity for generalization, they lack the processing power given by computers.
That is, to process a large amount of information (e.g., images) in a reduced interval of time.
The separation of an image into its components (i.e., join pixels into regions) according to some features is called image segmentation [1]}.
Reproducing this process at or above the human level on a computer is not an easy task, and several approaches have been proposed to address it [2]}.
Nevertheless, the segmentation task continues to be challenging mainly due to variability, i.e., when the visual tasks are performed on a computer there is a considerable variation in pose, appearance, viewpoint, illumination, and occlusion throughout different instances of the same image.
Thus, a type of segmentation commonly used is semantic segmentation (SS).
SS is an essential part of the pipeline in computer vision projects.
It extracts and analyzes useful and meaningful information, in addition to classifying the regions obtained within an image.
In other words, by improving the segmentation stage, the computer vision's final output is also enhanced.
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